@inproceedings{0d4e968d3acf4a4a804798e03bd053a0,
title = "Broad learning for optimal short-term traffic flow prediction",
abstract = "In this work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but also in terms of testing accuracy.",
keywords = "Broad Learning System, Fast least-square methods, Flat network, Traffic flow prediction",
author = "Di Liu and Wenwu Yu and Simone Baldi",
year = "2019",
doi = "10.1007/978-3-030-22796-8_25",
language = "English",
isbn = "978-3-030-22795-1",
series = "Lecture Notes in Computer Science (LNCS)",
publisher = "Springer",
pages = "232--239",
editor = "Huchuan Lu and Huajin Tang and Zhanshan Wang",
booktitle = "Advances in Neural Networks",
note = "16th International Symposium on Neural Networks, ISNN 2019 ; Conference date: 10-07-2019 Through 12-07-2019",
}